no code implementations • 12 Mar 2024 • Likun Li, Haoqi Zeng, Changpeng Yang, Haozhe Jia, Di Xu
The objective of personalization and stylization in text-to-image is to instruct a pre-trained diffusion model to analyze new concepts introduced by users and incorporate them into expected styles.
no code implementations • 11 Dec 2023 • Haozhe Jia, Yan Li, Hengfei Cui, Di Xu, Changpeng Yang, Yuwang Wang, Tao Yu
Our DisControlNet can perform robust editing on any facial image through training on large-scale 2D in-the-wild portraits and also supports low-cost fine-tuning with few additional images to further learn diverse personalized priors of a specific person.
no code implementations • 10 Feb 2022 • Haozhe Jia, Chao Bai, Weidong Cai, Heng Huang, Yong Xia
In our previous work, $i. e.$, HNF-Net, high-resolution feature representation and light-weight non-local self-attention mechanism are exploited for brain tumor segmentation using multi-modal MR imaging.
no code implementations • 9 Aug 2021 • Haozhe Jia, Haoteng Tang, Guixiang Ma, Weidong Cai, Heng Huang, Liang Zhan, Yong Xia
In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the segmentation backbone, and then converted into a sparsely-connected graph by keeping only K strongest connections to each uncertain pixel.
no code implementations • 9 Aug 2021 • Haoteng Tang, Haozhe Jia, Weidong Cai, Heng Huang, Yong Xia, Liang Zhan
In this paper, we propose a Boundary-aware Graph Reasoning (BGR) module to learn long-range contextual features for semantic segmentation.
no code implementations • 30 Dec 2020 • Haozhe Jia, Weidong Cai, Heng Huang, Yong Xia
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images.
no code implementations • MICCAI 2018 2018 • Donghao Zhang, Yang song, Dongnan Liu, Haozhe Jia, Si-Qi Liu, Yong Xia, Heng Huang, Weidong Cai
The morphological clues of various cancer cells are essential for pathologists to determine the stages of cancers.
Ranked #1 on Nuclear Segmentation on Cell17
no code implementations • 18 Jul 2018 • Haozhe Jia, Yang song, Donghao Zhang, Heng Huang, Dagan Feng, Michael Fulham, Yong Xia, Weidong Cai
In this paper, we propose a 3D Global Convolutional Adversarial Network (3D GCA-Net) to address efficient prostate MR volume segmentation.